import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu




class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	List[Any]:


   """simple docstring"""







   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()







 @property
 def 		__UpperCAmelCase				(		self				:   Optional[Any])						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    1
   _UpperCamelCase           =    3
   _UpperCamelCase           =    (32, 32)

   _UpperCamelCase           =    floats_tensor((batch_size, num_channels) + sizes							, rng=random.Random(0)).to(lowercase_)
   return image







 @property
 def 		__UpperCAmelCase				(		self				:   int)						->	int:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase           =    UNetaDConditionModel(
       block_out_channels=(32, 64)							, layers_per_block=2							, sample_size=32							, in_channels=4							, out_channels=4							, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")							, up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")							, cross_attention_dim=32							, )
   return model







 @property
 def 		__UpperCAmelCase				(		self				:   Dict)						->	List[Any]:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase           =    AutoencoderKL(
       block_out_channels=[32, 64]							, in_channels=3							, out_channels=3							, down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]							, up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]							, latent_channels=4							, )
   return model







 @property
 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Optional[int]:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase           =    CLIPTextConfig(
       bos_token_id=0							, eos_token_id=2							, hidden_size=32							, intermediate_size=37							, layer_norm_eps=1e-0_5							, num_attention_heads=4							, num_hidden_layers=5							, pad_token_id=1							, vocab_size=1000							, )
   return CLIPTextModel(lowercase_)







 @property
 def 		__UpperCAmelCase				(		self				:   Union[str, Any])						->	List[str]:


   """simple docstring"""







   def extract(*lowercase_				:   int							, **lowercase_				:   Optional[Any]):




     class 					_UpperCAmelCase			:



      '''simple docstring'''




      def __init__(		self				:   List[Any])						->	Dict:


        """simple docstring"""







        _UpperCamelCase           =    torch.ones([0])







      def 		__UpperCAmelCase				(		self				:   str							, lowercase_				:   Optional[int])						->	Optional[int]:


        """simple docstring"""







        self.pixel_values.to(lowercase_)
        return self


     return Out()

   return extract







 def 		__UpperCAmelCase				(		self				:   Optional[Any])						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    "cpu"  # ensure determinism for the device-dependent torch.Generator
   _UpperCamelCase           =    self.dummy_cond_unet
   _UpperCamelCase           =    DDIMScheduler(
       beta_start=0.0_00_85							, beta_end=0.0_12							, beta_schedule="scaled_linear"							, clip_sample=lowercase_							, set_alpha_to_one=lowercase_							, )

   _UpperCamelCase           =    self.dummy_vae
   _UpperCamelCase           =    self.dummy_text_encoder
   _UpperCamelCase           =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

   # make sure here that pndm scheduler skips prk
   _UpperCamelCase           =    StableDiffusionPipeline(
       unet=lowercase_							, scheduler=lowercase_							, vae=lowercase_							, text_encoder=lowercase_							, tokenizer=lowercase_							, safety_checker=lowercase_							, feature_extractor=self.dummy_extractor							, )
   _UpperCamelCase           =    sd_pipe.to(lowercase_)
   sd_pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    "A painting of a squirrel eating a burger"

   _UpperCamelCase           =    torch.Generator(device=lowercase_).manual_seed(0)
   _UpperCamelCase           =    sd_pipe([prompt]							, generator=lowercase_							, guidance_scale=6.0							, num_inference_steps=2							, output_type="np")
   _UpperCamelCase           =    output.images

   _UpperCamelCase           =    torch.Generator(device=lowercase_).manual_seed(0)
   _UpperCamelCase           =    sd_pipe(
       [prompt]							, generator=lowercase_							, guidance_scale=6.0							, num_inference_steps=2							, output_type="np"							, return_dict=lowercase_							, )[0]

   _UpperCamelCase           =    image[0, -3:, -3:, -1]
   _UpperCamelCase           =    image_from_tuple[0, -3:, -3:, -1]

   assert image.shape == (1, 64, 64, 3)
   _UpperCamelCase           =    np.array([0.57_56, 0.61_18, 0.50_05, 0.50_41, 0.54_71, 0.47_26, 0.49_76, 0.48_65, 0.48_64])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
   assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2







 def 		__UpperCAmelCase				(		self				:   Dict)						->	Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase           =    "cpu"  # ensure determinism for the device-dependent torch.Generator
   _UpperCamelCase           =    self.dummy_cond_unet
   _UpperCamelCase           =    PNDMScheduler(skip_prk_steps=lowercase_)
   _UpperCamelCase           =    self.dummy_vae
   _UpperCamelCase           =    self.dummy_text_encoder
   _UpperCamelCase           =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

   # make sure here that pndm scheduler skips prk
   _UpperCamelCase           =    StableDiffusionPipeline(
       unet=lowercase_							, scheduler=lowercase_							, vae=lowercase_							, text_encoder=lowercase_							, tokenizer=lowercase_							, safety_checker=lowercase_							, feature_extractor=self.dummy_extractor							, )
   _UpperCamelCase           =    sd_pipe.to(lowercase_)
   sd_pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    "A painting of a squirrel eating a burger"
   _UpperCamelCase           =    torch.Generator(device=lowercase_).manual_seed(0)
   _UpperCamelCase           =    sd_pipe([prompt]							, generator=lowercase_							, guidance_scale=6.0							, num_inference_steps=2							, output_type="np")

   _UpperCamelCase           =    output.images

   _UpperCamelCase           =    torch.Generator(device=lowercase_).manual_seed(0)
   _UpperCamelCase           =    sd_pipe(
       [prompt]							, generator=lowercase_							, guidance_scale=6.0							, num_inference_steps=2							, output_type="np"							, return_dict=lowercase_							, )[0]

   _UpperCamelCase           =    image[0, -3:, -3:, -1]
   _UpperCamelCase           =    image_from_tuple[0, -3:, -3:, -1]

   assert image.shape == (1, 64, 64, 3)
   _UpperCamelCase           =    np.array([0.51_25, 0.57_16, 0.48_28, 0.50_60, 0.56_50, 0.47_68, 0.51_85, 0.48_95, 0.49_93])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
   assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    StableDiffusionPipeline.from_pretrained(
       "hf-internal-testing/tiny-stable-diffusion-lms-pipe"							, safety_checker=lowercase_)
   assert isinstance(lowercase_							, lowercase_)
   assert isinstance(pipe.scheduler							, lowercase_)
   assert pipe.safety_checker is None

   _UpperCamelCase           =    pipe("example prompt"							, num_inference_steps=2).images[0]
   assert image is not None

   # check that there's no error when saving a pipeline with one of the models being None
   with tempfile.TemporaryDirectory() as tmpdirname:
     pipe.save_pretrained(lowercase_)
     _UpperCamelCase           =    StableDiffusionPipeline.from_pretrained(lowercase_)

   # sanity check that the pipeline still works
   assert pipe.safety_checker is None
   _UpperCamelCase           =    pipe("example prompt"							, num_inference_steps=2).images[0]
   assert image is not None







 @unittest.skipIf(torch_device != "cuda"							, "This test requires a GPU")
 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    self.dummy_cond_unet
   _UpperCamelCase           =    PNDMScheduler(skip_prk_steps=lowercase_)
   _UpperCamelCase           =    self.dummy_vae
   _UpperCamelCase           =    self.dummy_text_encoder
   _UpperCamelCase           =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

   # put models in fp16
   _UpperCamelCase           =    unet.half()
   _UpperCamelCase           =    vae.half()
   _UpperCamelCase           =    bert.half()

   # make sure here that pndm scheduler skips prk
   _UpperCamelCase           =    StableDiffusionPipeline(
       unet=lowercase_							, scheduler=lowercase_							, vae=lowercase_							, text_encoder=lowercase_							, tokenizer=lowercase_							, safety_checker=lowercase_							, feature_extractor=self.dummy_extractor							, )
   _UpperCamelCase           =    sd_pipe.to(lowercase_)
   sd_pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    "A painting of a squirrel eating a burger"
   _UpperCamelCase           =    sd_pipe([prompt]							, num_inference_steps=2							, output_type="np").images

   assert image.shape == (1, 64, 64, 3)




@nightly
@require_torch_gpu
class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Optional[Any]:


   """simple docstring"""







   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"							, safety_checker=lowercase_)
   _UpperCamelCase           =    LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
   _UpperCamelCase           =    sd_pipe.to(lowercase_)
   sd_pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    (
       "portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"
       " coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"
       " anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"
       " children from bahnhof zoo, detailed "
   )
   _UpperCamelCase           =    4003660346
   _UpperCamelCase           =    7

   # without safety guidance (sld_guidance_scale = 0)
   _UpperCamelCase           =    torch.manual_seed(lowercase_)
   _UpperCamelCase           =    sd_pipe(
       [prompt]							, generator=lowercase_							, guidance_scale=lowercase_							, num_inference_steps=50							, output_type="np"							, width=512							, height=512							, sld_guidance_scale=0							, )

   _UpperCamelCase           =    output.images
   _UpperCamelCase           =    image[0, -3:, -3:, -1]
   _UpperCamelCase           =    [0.22_78, 0.22_31, 0.22_49, 0.23_33, 0.23_03, 0.18_85, 0.22_73, 0.21_44, 0.21_76]

   assert image.shape == (1, 512, 512, 3)

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

   # without safety guidance (strong configuration)
   _UpperCamelCase           =    torch.manual_seed(lowercase_)
   _UpperCamelCase           =    sd_pipe(
       [prompt]							, generator=lowercase_							, guidance_scale=lowercase_							, num_inference_steps=50							, output_type="np"							, width=512							, height=512							, sld_guidance_scale=2000							, sld_warmup_steps=7							, sld_threshold=0.0_25							, sld_momentum_scale=0.5							, sld_mom_beta=0.7							, )

   _UpperCamelCase           =    output.images
   _UpperCamelCase           =    image[0, -3:, -3:, -1]
   _UpperCamelCase           =    [0.23_83, 0.22_76, 0.2_36, 0.21_92, 0.21_86, 0.20_53, 0.19_71, 0.19_01, 0.17_19]

   assert image.shape == (1, 512, 512, 3)

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase           =    StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"							, safety_checker=lowercase_)
   _UpperCamelCase           =    LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
   _UpperCamelCase           =    sd_pipe.to(lowercase_)
   sd_pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    "padme amidala taking a bath artwork, safe for work, no nudity"
   _UpperCamelCase           =    2734971755
   _UpperCamelCase           =    7

   _UpperCamelCase           =    torch.manual_seed(lowercase_)
   _UpperCamelCase           =    sd_pipe(
       [prompt]							, generator=lowercase_							, guidance_scale=lowercase_							, num_inference_steps=50							, output_type="np"							, width=512							, height=512							, sld_guidance_scale=0							, )

   _UpperCamelCase           =    output.images
   _UpperCamelCase           =    image[0, -3:, -3:, -1]
   _UpperCamelCase           =    [0.35_02, 0.36_22, 0.33_96, 0.36_42, 0.34_78, 0.33_18, 0.35, 0.33_48, 0.32_97]

   assert image.shape == (1, 512, 512, 3)

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

   _UpperCamelCase           =    torch.manual_seed(lowercase_)
   _UpperCamelCase           =    sd_pipe(
       [prompt]							, generator=lowercase_							, guidance_scale=lowercase_							, num_inference_steps=50							, output_type="np"							, width=512							, height=512							, sld_guidance_scale=2000							, sld_warmup_steps=7							, sld_threshold=0.0_25							, sld_momentum_scale=0.5							, sld_mom_beta=0.7							, )

   _UpperCamelCase           =    output.images
   _UpperCamelCase           =    image[0, -3:, -3:, -1]
   _UpperCamelCase           =    [0.55_31, 0.52_06, 0.48_95, 0.51_56, 0.51_82, 0.47_51, 0.48_02, 0.48_03, 0.44_43]

   assert image.shape == (1, 512, 512, 3)

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	List[str]:


   """simple docstring"""







   _UpperCamelCase           =    StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
   _UpperCamelCase           =    sd_pipe.to(lowercase_)
   sd_pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    (
       "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
       " leyendecker"
   )
   _UpperCamelCase           =    1044355234
   _UpperCamelCase           =    12

   _UpperCamelCase           =    torch.manual_seed(lowercase_)
   _UpperCamelCase           =    sd_pipe(
       [prompt]							, generator=lowercase_							, guidance_scale=lowercase_							, num_inference_steps=50							, output_type="np"							, width=512							, height=512							, sld_guidance_scale=0							, )

   _UpperCamelCase           =    output.images
   _UpperCamelCase           =    image[0, -3:, -3:, -1]
   _UpperCamelCase           =    np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])

   assert image.shape == (1, 512, 512, 3)

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-7

   _UpperCamelCase           =    torch.manual_seed(lowercase_)
   _UpperCamelCase           =    sd_pipe(
       [prompt]							, generator=lowercase_							, guidance_scale=lowercase_							, num_inference_steps=50							, output_type="np"							, width=512							, height=512							, sld_guidance_scale=2000							, sld_warmup_steps=7							, sld_threshold=0.0_25							, sld_momentum_scale=0.5							, sld_mom_beta=0.7							, )

   _UpperCamelCase           =    output.images
   _UpperCamelCase           =    image[0, -3:, -3:, -1]
   _UpperCamelCase           =    np.array([0.58_18, 0.62_85, 0.68_35, 0.60_19, 0.6_25, 0.67_54, 0.60_96, 0.63_34, 0.65_61])
   assert image.shape == (1, 512, 512, 3)

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2





from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
   from .pipeline_kandinsky import KandinskyPipeline
   from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
   from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
   from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
   from .text_encoder import MultilingualCLIP




